{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 黑体\n",
    "plt.rcParams['axes.unicode_minus'] = False    # 解决无法显示符号的问题\n",
    "sns.set(font='SimHei', font_scale=1.5)        # 解决Seaborn中文显示问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取数据集\n",
    "df = pd.read_csv(\"My Uber Drives - 2016.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>START_DATE*</th>\n",
       "      <th>END_DATE*</th>\n",
       "      <th>CATEGORY*</th>\n",
       "      <th>START*</th>\n",
       "      <th>STOP*</th>\n",
       "      <th>MILES*</th>\n",
       "      <th>PURPOSE*</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2016 21:11</td>\n",
       "      <td>1/1/2016 21:17</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>5.1</td>\n",
       "      <td>Meal/Entertain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2016 1:25</td>\n",
       "      <td>1/2/2016 1:37</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/2/2016 20:25</td>\n",
       "      <td>1/2/2016 20:38</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Errand/Supplies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/5/2016 17:31</td>\n",
       "      <td>1/5/2016 17:45</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Meeting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/6/2016 14:42</td>\n",
       "      <td>1/6/2016 15:49</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>63.7</td>\n",
       "      <td>Customer Visit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      START_DATE*       END_DATE* CATEGORY*       START*            STOP*  \\\n",
       "0  1/1/2016 21:11  1/1/2016 21:17  Business  Fort Pierce      Fort Pierce   \n",
       "1   1/2/2016 1:25   1/2/2016 1:37  Business  Fort Pierce      Fort Pierce   \n",
       "2  1/2/2016 20:25  1/2/2016 20:38  Business  Fort Pierce      Fort Pierce   \n",
       "3  1/5/2016 17:31  1/5/2016 17:45  Business  Fort Pierce      Fort Pierce   \n",
       "4  1/6/2016 14:42  1/6/2016 15:49  Business  Fort Pierce  West Palm Beach   \n",
       "\n",
       "   MILES*         PURPOSE*  \n",
       "0     5.1   Meal/Entertain  \n",
       "1     5.0              NaN  \n",
       "2     4.8  Errand/Supplies  \n",
       "3     4.7          Meeting  \n",
       "4    63.7   Customer Visit  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 观察数据\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Business', 'Personal', nan], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['CATEGORY*'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>开始时间</th>\n",
       "      <th>结束时间</th>\n",
       "      <th>类别</th>\n",
       "      <th>起始位置</th>\n",
       "      <th>结束位置</th>\n",
       "      <th>里程</th>\n",
       "      <th>目的</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2016 21:11</td>\n",
       "      <td>1/1/2016 21:17</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>5.1</td>\n",
       "      <td>Meal/Entertain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2016 1:25</td>\n",
       "      <td>1/2/2016 1:37</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/2/2016 20:25</td>\n",
       "      <td>1/2/2016 20:38</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Errand/Supplies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/5/2016 17:31</td>\n",
       "      <td>1/5/2016 17:45</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Meeting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/6/2016 14:42</td>\n",
       "      <td>1/6/2016 15:49</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>63.7</td>\n",
       "      <td>Customer Visit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             开始时间            结束时间        类别         起始位置             结束位置  \\\n",
       "0  1/1/2016 21:11  1/1/2016 21:17  Business  Fort Pierce      Fort Pierce   \n",
       "1   1/2/2016 1:25   1/2/2016 1:37  Business  Fort Pierce      Fort Pierce   \n",
       "2  1/2/2016 20:25  1/2/2016 20:38  Business  Fort Pierce      Fort Pierce   \n",
       "3  1/5/2016 17:31  1/5/2016 17:45  Business  Fort Pierce      Fort Pierce   \n",
       "4  1/6/2016 14:42  1/6/2016 15:49  Business  Fort Pierce  West Palm Beach   \n",
       "\n",
       "     里程               目的  \n",
       "0   5.1   Meal/Entertain  \n",
       "1   5.0              NaN  \n",
       "2   4.8  Errand/Supplies  \n",
       "3   4.7          Meeting  \n",
       "4  63.7   Customer Visit  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = ['开始时间','结束时间','类别','起始位置','结束位置','里程','目的']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1156, 7)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1156 entries, 0 to 1155\n",
      "Data columns (total 7 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   开始时间    1156 non-null   object \n",
      " 1   结束时间    1155 non-null   object \n",
      " 2   类别      1155 non-null   object \n",
      " 3   起始位置    1155 non-null   object \n",
      " 4   结束位置    1155 non-null   object \n",
      " 5   里程      1156 non-null   float64\n",
      " 6   目的      653 non-null    object \n",
      "dtypes: float64(1), object(6)\n",
      "memory usage: 63.3+ KB\n"
     ]
    }
   ],
   "source": [
    "# 查看数据类型\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据清洗"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "开始时间      0\n",
       "结束时间      1\n",
       "类别        1\n",
       "起始位置      1\n",
       "结束位置      1\n",
       "里程        0\n",
       "目的      503\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 缺失值查看\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 剔除“结束时间”为空的记录\n",
    "data = df[df['结束时间'].isnull() == False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 异常值处理\n",
    "# 将“开始时间”等于“结束时间”视为无效记录\n",
    "data = data[data['开始时间'] != data['结束时间']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>开始时间</th>\n",
       "      <th>结束时间</th>\n",
       "      <th>类别</th>\n",
       "      <th>起始位置</th>\n",
       "      <th>结束位置</th>\n",
       "      <th>里程</th>\n",
       "      <th>目的</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>6/28/2016 23:34</td>\n",
       "      <td>6/28/2016 23:59</td>\n",
       "      <td>Business</td>\n",
       "      <td>Durham</td>\n",
       "      <td>Cary</td>\n",
       "      <td>9.9</td>\n",
       "      <td>Meeting</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                开始时间             结束时间        类别    起始位置  结束位置   里程       目的\n",
       "492  6/28/2016 23:34  6/28/2016 23:59  Business  Durham  Cary  9.9  Meeting"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 找到重复记录并删除\n",
    "data[data.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "开始时间      0\n",
       "结束时间      0\n",
       "类别        0\n",
       "起始位置      0\n",
       "结束位置      0\n",
       "里程        0\n",
       "目的      498\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1150 entries, 0 to 1154\n",
      "Data columns (total 7 columns):\n",
      " #   Column  Non-Null Count  Dtype         \n",
      "---  ------  --------------  -----         \n",
      " 0   开始时间    1150 non-null   datetime64[ns]\n",
      " 1   结束时间    1150 non-null   datetime64[ns]\n",
      " 2   类别      1150 non-null   object        \n",
      " 3   起始位置    1150 non-null   object        \n",
      " 4   结束位置    1150 non-null   object        \n",
      " 5   里程      1150 non-null   float64       \n",
      " 6   目的      652 non-null    object        \n",
      "dtypes: datetime64[ns](2), float64(1), object(4)\n",
      "memory usage: 71.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# 将\"开始日期\"和\"结束日期\"转换为日期时间格式\n",
    "data['开始时间'] = pd.to_datetime(data['开始时间'])\n",
    "data['结束时间'] = pd.to_datetime(data['结束时间'])\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 运营分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 总体运营情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>月</th>\n",
       "      <th>count</th>\n",
       "      <th>last_count</th>\n",
       "      <th>环比增长率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>115</td>\n",
       "      <td>61</td>\n",
       "      <td>0.885246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>113</td>\n",
       "      <td>115</td>\n",
       "      <td>-0.017391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>54</td>\n",
       "      <td>113</td>\n",
       "      <td>-0.522124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>49</td>\n",
       "      <td>54</td>\n",
       "      <td>-0.092593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>107</td>\n",
       "      <td>49</td>\n",
       "      <td>1.183673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>112</td>\n",
       "      <td>107</td>\n",
       "      <td>0.046729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>133</td>\n",
       "      <td>112</td>\n",
       "      <td>0.187500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>34</td>\n",
       "      <td>133</td>\n",
       "      <td>-0.744361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>104</td>\n",
       "      <td>34</td>\n",
       "      <td>2.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>122</td>\n",
       "      <td>104</td>\n",
       "      <td>0.173077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>146</td>\n",
       "      <td>122</td>\n",
       "      <td>0.196721</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     月  count  last_count     环比增长率\n",
       "0    1     61           0       inf\n",
       "1    2    115          61  0.885246\n",
       "2    3    113         115 -0.017391\n",
       "3    4     54         113 -0.522124\n",
       "4    5     49          54 -0.092593\n",
       "5    6    107          49  1.183673\n",
       "6    7    112         107  0.046729\n",
       "7    8    133         112  0.187500\n",
       "8    9     34         133 -0.744361\n",
       "9   10    104          34  2.058824\n",
       "10  11    122         104  0.173077\n",
       "11  12    146         122  0.196721"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['月'] = data['开始时间'].dt.month\n",
    "data_month_c = data.groupby('月', as_index=False)['开始时间'].count()\n",
    "data_month_c.columns = ['月','count']\n",
    "data_month_c['last_count'] = 0\n",
    "\n",
    "for i in range(len(data_month_c)):\n",
    "    if i == 0:\n",
    "        data_month_c['last_count'][i] = 0\n",
    "    else:\n",
    "        data_month_c['last_count'][i] = data_month_c['count'][i-1]\n",
    "        \n",
    "data_month_c['环比增长率'] = (data_month_c['count'] - data_month_c['last_count'])/data_month_c['last_count']\n",
    "data_month_c\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "\n",
    "ax = sns.barplot(x=\"月\", y=\"count\", data=data_month_c)\n",
    "\n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(data_month_c.index, data_month_c[\"环比增长率\"], color=\"C1\", marker=\"D\", ms=7)\n",
    "ax2.tick_params(axis=\"y\", colors=\"C1\")\n",
    "ax.set_title('Uber订单变化')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2016年1-12月，Uber业务呈季节性波动，需结合业务实际具体分析原因"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 用户出行特征分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 出行类别频率分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_gatecory_cnt = data.groupby('类别', as_index = False)['开始时间'].count()\n",
    "data_gatecory_cnt.columns = ['类别','数量']\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "ax = sns.barplot(x=\"类别\", y=\"数量\", data=data_gatecory_cnt)\n",
    "ax.set_title('Business vs Personal')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "出行绝大多数以Business为主"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 跨城与同城的订单分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_round(x):\n",
    "    if x['起始位置'] == x['结束位置']:\n",
    "        return 'Y'\n",
    "    else:\n",
    "        return 'N'\n",
    "\n",
    "data['同城出行'] = data.apply(is_round, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>同城出行</th>\n",
       "      <th>类别</th>\n",
       "      <th>开始时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>N</td>\n",
       "      <td>Business</td>\n",
       "      <td>811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>N</td>\n",
       "      <td>Personal</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Y</td>\n",
       "      <td>Business</td>\n",
       "      <td>262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Y</td>\n",
       "      <td>Personal</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  同城出行        类别  开始时间\n",
       "0    N  Business   811\n",
       "1    N  Personal    55\n",
       "2    Y  Business   262\n",
       "3    Y  Personal    22"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(['同城出行','类别'], as_index=False)['开始时间'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_round_cnt = data.groupby(['同城出行','类别'], as_index=False)['开始时间'].count()\n",
    "data_round_cnt.columns = ['同城出行','类别','count']\n",
    "data_round_cnt = data_round_cnt.sort_values('count', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>同城出行</th>\n",
       "      <th>类别</th>\n",
       "      <th>count</th>\n",
       "      <th>count_per</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>N</td>\n",
       "      <td>Business</td>\n",
       "      <td>811</td>\n",
       "      <td>0.705217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Y</td>\n",
       "      <td>Business</td>\n",
       "      <td>262</td>\n",
       "      <td>0.227826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>N</td>\n",
       "      <td>Personal</td>\n",
       "      <td>55</td>\n",
       "      <td>0.047826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Y</td>\n",
       "      <td>Personal</td>\n",
       "      <td>22</td>\n",
       "      <td>0.019130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  同城出行        类别  count  count_per\n",
       "0    N  Business    811   0.705217\n",
       "2    Y  Business    262   0.227826\n",
       "1    N  Personal     55   0.047826\n",
       "3    Y  Personal     22   0.019130"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_round_cnt['count_per'] = data_round_cnt['count']/data_round_cnt['count'].sum()\n",
    "data_round_cnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "\n",
    "ax = sns.lineplot(data_round_cnt['同城出行'], data_round_cnt[\"count\"], hue=data_round_cnt[\"类别\"], style=data_round_cnt[\"类别\"])\n",
    "\n",
    "ax.set_title('跨城与同城的订单分析')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过将订单情况按照订单类型来进行分群分析发现，因商务而叫车的跨城出行订单数量占绝大多数，占比70.52%，说明因公跨城出行需求较大。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 出行目的帕累托分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_purpose_cnt = data.groupby('目的', as_index=False)['开始时间'].count()\n",
    "data_purpose_cnt.columns = ['目的','数量']\n",
    "data_purpose_cnt = data_purpose_cnt.sort_values('数量', ascending=False)\n",
    "data_purpose_cnt.reset_index(inplace=True)\n",
    "data_purpose_cnt['cumpercentage'] = data_purpose_cnt['数量'].cumsum()/data_purpose_cnt['数量'].sum()*100\n",
    "\n",
    "plt.figure(figsize=(15,6))\n",
    "\n",
    "ax = sns.barplot(x=\"目的\", y=\"数量\", data=data_purpose_cnt, palette='rocket')\n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(data_purpose_cnt.index, data_purpose_cnt[\"cumpercentage\"], color=\"C1\", marker=\"D\", ms=7)\n",
    "ax2.tick_params(axis=\"y\", colors=\"C1\")\n",
    "ax.set_title('出行目的帕累托分析')\n",
    "plt.setp(ax.get_xticklabels(), rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图可知，Meeting、Meal/Entertain、Errand/Supplies和Customer Visit的数量更多，占据80%以上，出行目的排名第一的Meeting贡献了28.52%的订单"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>目的</th>\n",
       "      <th>数量</th>\n",
       "      <th>cumpercentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>Meeting</td>\n",
       "      <td>186</td>\n",
       "      <td>28.527607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>Meal/Entertain</td>\n",
       "      <td>160</td>\n",
       "      <td>53.067485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>Errand/Supplies</td>\n",
       "      <td>128</td>\n",
       "      <td>72.699387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Customer Visit</td>\n",
       "      <td>101</td>\n",
       "      <td>88.190184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>Temporary Site</td>\n",
       "      <td>50</td>\n",
       "      <td>95.858896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>Between Offices</td>\n",
       "      <td>18</td>\n",
       "      <td>98.619632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>Moving</td>\n",
       "      <td>4</td>\n",
       "      <td>99.233129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>Airport/Travel</td>\n",
       "      <td>3</td>\n",
       "      <td>99.693252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>Charity ($)</td>\n",
       "      <td>1</td>\n",
       "      <td>99.846626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>Commute</td>\n",
       "      <td>1</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index               目的   数量  cumpercentage\n",
       "0      7          Meeting  186      28.527607\n",
       "1      6   Meal/Entertain  160      53.067485\n",
       "2      5  Errand/Supplies  128      72.699387\n",
       "3      4   Customer Visit  101      88.190184\n",
       "4      9   Temporary Site   50      95.858896\n",
       "5      1  Between Offices   18      98.619632\n",
       "6      8           Moving    4      99.233129\n",
       "7      0   Airport/Travel    3      99.693252\n",
       "8      2      Charity ($)    1      99.846626\n",
       "9      3          Commute    1     100.000000"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_purpose_cnt.sort_values('数量', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 出行订单月份分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "dic_month = {\n",
    "    1:'一月',\n",
    "    2:'二月',\n",
    "    3:'三月',\n",
    "    4:'四月',\n",
    "    5:'五月',\n",
    "    6:'六月',\n",
    "    7:'七月',\n",
    "    8:'八月',\n",
    "    9:'九月',\n",
    "    10:'十月',\n",
    "    11:'十一月',\n",
    "    12:'十二月'\n",
    "}\n",
    "data['月份'] = data['开始时间'].dt.month.map(dic_month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>开始时间</th>\n",
       "      <th>结束时间</th>\n",
       "      <th>类别</th>\n",
       "      <th>起始位置</th>\n",
       "      <th>结束位置</th>\n",
       "      <th>里程</th>\n",
       "      <th>目的</th>\n",
       "      <th>月</th>\n",
       "      <th>月份</th>\n",
       "      <th>星期几</th>\n",
       "      <th>小时</th>\n",
       "      <th>里程打标</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01 21:11:00</td>\n",
       "      <td>2016-01-01 21:17:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>5.1</td>\n",
       "      <td>Meal/Entertain</td>\n",
       "      <td>1</td>\n",
       "      <td>一月</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>0-10 miles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-02 01:25:00</td>\n",
       "      <td>2016-01-02 01:37:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>一月</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0-10 miles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-02 20:25:00</td>\n",
       "      <td>2016-01-02 20:38:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Errand/Supplies</td>\n",
       "      <td>1</td>\n",
       "      <td>一月</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>0-10 miles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-05 17:31:00</td>\n",
       "      <td>2016-01-05 17:45:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Meeting</td>\n",
       "      <td>1</td>\n",
       "      <td>一月</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>0-10 miles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-06 14:42:00</td>\n",
       "      <td>2016-01-06 15:49:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>63.7</td>\n",
       "      <td>Customer Visit</td>\n",
       "      <td>1</td>\n",
       "      <td>一月</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>above 50 miles</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 开始时间                结束时间        类别         起始位置  \\\n",
       "0 2016-01-01 21:11:00 2016-01-01 21:17:00  Business  Fort Pierce   \n",
       "1 2016-01-02 01:25:00 2016-01-02 01:37:00  Business  Fort Pierce   \n",
       "2 2016-01-02 20:25:00 2016-01-02 20:38:00  Business  Fort Pierce   \n",
       "3 2016-01-05 17:31:00 2016-01-05 17:45:00  Business  Fort Pierce   \n",
       "4 2016-01-06 14:42:00 2016-01-06 15:49:00  Business  Fort Pierce   \n",
       "\n",
       "              结束位置    里程               目的  月  月份  星期几  小时            里程打标  \n",
       "0      Fort Pierce   5.1   Meal/Entertain  1  一月    5  21      0-10 miles  \n",
       "1      Fort Pierce   5.0              NaN  1  一月    6   1      0-10 miles  \n",
       "2      Fort Pierce   4.8  Errand/Supplies  1  一月    6  20      0-10 miles  \n",
       "3      Fort Pierce   4.7          Meeting  1  一月    2  17      0-10 miles  \n",
       "4  West Palm Beach  63.7   Customer Visit  1  一月    3  14  above 50 miles  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_month_cnt = data.groupby('月份', as_index=False)['开始时间'].count()\n",
    "data_month_cnt.columns = ['月份','count']\n",
    "data_month_cnt = data_month_cnt.sort_values('count', ascending=False)\n",
    "data_month_cnt.reset_index(inplace=True)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(15,6))\n",
    "\n",
    "ax = sns.barplot(x=\"月份\", y=\"count\", data=data_month_cnt, palette='rocket')\n",
    "\n",
    "\n",
    "ax.set_title('出行订单月份分布')\n",
    "plt.setp(ax.get_xticklabels(), rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "按月订单，在十二月和八月更多，在一月、四月、五月和九月相对较少，主要考虑由于淡旺季、节假日带来的季节性影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>开始时间</th>\n",
       "      <th>结束时间</th>\n",
       "      <th>类别</th>\n",
       "      <th>起始位置</th>\n",
       "      <th>结束位置</th>\n",
       "      <th>里程</th>\n",
       "      <th>目的</th>\n",
       "      <th>月</th>\n",
       "      <th>月份</th>\n",
       "      <th>星期几</th>\n",
       "      <th>小时</th>\n",
       "      <th>里程打标</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01 21:11:00</td>\n",
       "      <td>2016-01-01 21:17:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>5.1</td>\n",
       "      <td>Meal/Entertain</td>\n",
       "      <td>1</td>\n",
       "      <td>一月</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>0-10 miles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-02 01:25:00</td>\n",
       "      <td>2016-01-02 01:37:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>一月</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0-10 miles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-02 20:25:00</td>\n",
       "      <td>2016-01-02 20:38:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Errand/Supplies</td>\n",
       "      <td>1</td>\n",
       "      <td>一月</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>0-10 miles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-05 17:31:00</td>\n",
       "      <td>2016-01-05 17:45:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Meeting</td>\n",
       "      <td>1</td>\n",
       "      <td>一月</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>0-10 miles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-06 14:42:00</td>\n",
       "      <td>2016-01-06 15:49:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>63.7</td>\n",
       "      <td>Customer Visit</td>\n",
       "      <td>1</td>\n",
       "      <td>一月</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>above 50 miles</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 开始时间                结束时间        类别         起始位置  \\\n",
       "0 2016-01-01 21:11:00 2016-01-01 21:17:00  Business  Fort Pierce   \n",
       "1 2016-01-02 01:25:00 2016-01-02 01:37:00  Business  Fort Pierce   \n",
       "2 2016-01-02 20:25:00 2016-01-02 20:38:00  Business  Fort Pierce   \n",
       "3 2016-01-05 17:31:00 2016-01-05 17:45:00  Business  Fort Pierce   \n",
       "4 2016-01-06 14:42:00 2016-01-06 15:49:00  Business  Fort Pierce   \n",
       "\n",
       "              结束位置    里程               目的  月  月份  星期几  小时            里程打标  \n",
       "0      Fort Pierce   5.1   Meal/Entertain  1  一月    5  21      0-10 miles  \n",
       "1      Fort Pierce   5.0              NaN  1  一月    6   1      0-10 miles  \n",
       "2      Fort Pierce   4.8  Errand/Supplies  1  一月    6  20      0-10 miles  \n",
       "3      Fort Pierce   4.7          Meeting  1  一月    2  17      0-10 miles  \n",
       "4  West Palm Beach  63.7   Customer Visit  1  一月    3  14  above 50 miles  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取weekday\n",
    "data['星期几'] = data['开始时间'].dt.weekday + 1 # 将星期一视为一周第一天，从1开始\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 一周出行分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ginSLaVZWlkaNGiUPDw/17dvXHvQkqU6dOho4cKA+//xzPf3002ratKliY2MVFhamoKAgSVJoaKhat26twYMHKywsTDt37lTFihXVp0+fovwaAAAAAOCUijQgJiYm6tixY5Kk5557zqHv448/1mOPPabPP/9cc+bMUWJiogYNGqSXX37ZPsbFxUVLlizRwoULtXXrVrVq1Urjxo2TtzeHzgAAAADAr+VitVpL3WktHFIDAEDxVamSj/q/+onRZZRKK2Y9yyE1gJMrlofUAAAAAACKHwIiAAAAAEASAREAAAAAYENABAAAAABIIiACAAAAAGwIiAAAAAAASQREAAAAAIANAREAAAAAIImACAAAAACwISACAAAAACQREAEAAAAANgREAAAAAIAkAiIAAAAAwIaACAAAAACQREAEAAAAANgQEAEAAAAAkgiIAAAAAAAbAiIAAAAAQBIBEQAAAABgQ0AEAAAAAEgiIAIAAAAAbAiIAAAAAABJBEQAAAAAgA0BEQAAAAAgiYAIAAAAALBxN+qDd+zYob/85S/atGmTvS0wMDDXsTVq1NCWLVskSV26dNHx48cd+qdOnarevXsXWq0AAAAAUBoYEhCTkpIUFRUlLy8vh/bPPvssx9g5c+aoXLlykqS0tDSdOHFCMTExqlq1qn1MjRo1CrdgAAAAACgFijwg7t+/X0OGDFHNmjWVkpLi0BccHOzw86lTp/Tdd9/Zg+Phw4dlMpn0u9/9Tu7uhi1+AgAAAIBTKvJnEPft26fx48erf//+9x27ePFihYWFqX79+pKk+Ph4NWjQgHAIAAAAAIWgyAPi888/r/Dw8PuOS05O1hdffKHnn3/e3hYfH6+LFy+qY8eOaty4sXr16qVt27YVZrkAAAAAUGoUeUB0dc3bR65atUp169ZVSEiIve3AgQMqV66cxowZo4ULF6pSpUoaMWJEjkNrAAAAAAD5Vyz3alqtVq1cuVLDhg1zaJ8zZ46qV68uX19fSVJoaKi6du2qVatW6ZVXXsnz/f39vQu0XgAAAGdRqZKP0SUAMFCxDIjff/+9fvnlF3Xu3Nmh/daziLe4u7srODhYCQkJ+bp/cnKqLBZrnsb6+JZRGU9Tvu6PgpOekaVrV9ML7f7Mr7EKe34BlEwEFGNduHDN6BIAFCJXV5d7LpgVy4C4fv16NW/eXBUrVrS3ZWRkaN++fXr88ccdxl6+fFlZWVmFVksZT5P6v/pJod0f97Zi1rO6psILEMyvsQp7fgEAAJA/Rf4MYl5s3rxZHTp0cGhLSUlRZGSk4uPj7W23XoPRuHHjIq4QAAAAAJxPsVtBPHnypM6ePatmzZo5tFerVk1hYWEaNWqUIiIilJ2drWXLlsnLy0sDBgwwqFoAAAAAcB7FbgVxz549MplMatSoUY6+t956SyEhIXrnnXe0aNEiNWvWTF988YWqVatmQKUAAAAA4FwMW0EMDw/P9X2IvXv3Vu/evXO9xtfXV7Nnzy7s0gAAAACgVCp2K4gAAAAAAGMQEAEAAAAAkgiIAAAAAAAbAiIAAAAAQBIBEQAAAABgQ0AEAAAAAEgiIAIAAAAAbAiIAAAAAABJBEQAAAAAgA0BEQAAAAAgiYAIAAAAALAhIAIAAAAAJBEQAQAAAAA2BEQAAAAAgCQCIgAAAADAhoAIAAAAAJBEQAQAAAAA2BAQAQAAAACSCIgAAAAAABsCIgAAAABAEgERAAAAAGBDQAQAAAAASCIgAgAAAABsCIgAAAAAAEkERAAAAACAjWEBcceOHerUqZND2/nz5xUYGJjjz4kTJ+xjEhIS1K9fP4WEhCgiIkL/+9//irp0AAAAAHBK7kZ8aFJSkqKiouTl5eXQHh8fr4oVK2rp0qUO7dWqVZMkJScnKyIiQgEBAZo/f76++uorDR06VKtXr5bJZCqy+gEAxvLxLaMynvy9b5T0jCxdu5pudBkAgEJQ5AFx//79GjJkiGrWrKmUlBSHvvj4eAUGBio4ODjXa5ctWyaz2awlS5bIx8dHjz/+uDp37qxNmzapW7duRVE+AKAYKONpUv9XPzG6jFJrxaxndU0ERABwRkW+xXTfvn0aP368+vfvn6MvPj5eQUFBd712586dateunXx8fCRJrq6u6tChg3bt2lVo9QIAAABAaVHkAfH5559XeHh4rn3x8fHaunWrWrdurZCQEA0fPlzHjx+39996RvF21atXdxgDAAAAAHgwRR4QXV1z/8iUlBSdOXNGNWrU0MyZMzVjxgydOnVKQ4YMkdlsliRlZGTYVw9v8fT01KVLlwq9bgAAAABwdoYcUpMbb29vrVq1SkFBQfYQ2bBhQ3Xu3Fnbt29X+/btZTKZ5Obm5nCdh4eH0tPz9xyEv793gdWNwlepks/9B6HEYn6BkonfXefF3AKlW7EJiB4eHmrcuLFD2yOPPCJ/f38lJCSoffv28vf31/nz5x3GXL58WWXLls3XZyUnp8piseZpLH9JGu/ChWuFdm/m13iFOb9wXvzuGo+/m51XYc4tJxAbixOIIUmuri73XDArNgExOTlZJ0+eVEhIiL0tKytLqampysjIkCQ1adJEcXFxDtcdOnRIlStXLtJaAQAAkH+cQGwsTiBGXhSbgLh9+3bNnj1bmzZtUpkyZSRJ69evV3p6upo0aSJJ6tq1qyIjI3XgwAEFBwfr7Nmz2rJli0aPHm1g5QAAAABYITZOQa4OF5uA2LFjR82dO1dDhw7VU089pbNnz+qDDz5Q06ZN1bZtW0lSaGioWrdurcGDByssLEw7d+5UxYoV1adPH4OrB1Ac8R8q47CNCQBKH1aIjVOQq8PFJiB6e3vrvffe0+TJkzVlyhRVqFBBERERevHFF+2H1ri4uGjJkiVauHChtm7dqlatWmncuHHy9ubQGQA58R8q47CNCQCAksmwgBgeHp7jfYj16tXT8uXL73mdh4eHxowZozFjxhRmeQAAAABQ6hT5exABAAAAAMUTAREAAAAAIImACAAAAACwISACAAAAACQREAEAAAAANgREAAAAAIAkAiIAAAAAwIaACAAAAACQREAEAAAAANgQEAEAAAAAkgiIAAAAAAAbAiIAAAAAQBIBEQAAAABgQ0AEAAAAAEgiIAIAAAAAbAiIAAAAAABJBEQAAAAAgA0BEQAAAAAgiYAIAAAAALAhIAIAAAAAJBEQAQAAAAA2BEQAAAAAgCQCIgAAAADAhoAIAAAAAJBEQAQAAAAA2BgWEHfs2KFOnTo5tJnNZs2ePVuhoaEKCgpSjx49tH//focxXbp0UWBgoMOfVatWFWXpAAAAAOCU3I340KSkJEVFRcnLy8uhfeHChfrkk080aNAg1apVSx988IGGDx+uTZs2ycvLS2lpaTpx4oRiYmJUtWpV+3U1atQo6q8AAAAAAE6nyAPi/v37NWTIENWsWVMpKSn29tTUVP3tb3/T22+/bV9ZrF+/vnr27Kl9+/apffv2Onz4sEwmk373u9/J3d2QbAsAAAAATqvIU9a+ffs0fvx4SVJMTIy93cPDQ8uXL1dQUJC9zc/PT5KUlZUlSYqPj1eDBg0IhwAAAABQCIr8GcTnn39e4eHhOdo9PDwcwqEkbdu2Ta6urmrSpImkmwHx4sWL6tixoxo3bqxevXpp27ZtRVI3AAAAADi7Il+Kc3XNWybNyMjQX//6V3Xv3l2VKlWSJB04cEDlypXTsGHDVL58eX3yyScaMWKE1q5dq9q1a+e5Bn9/7wcpHQapVMnH6BJQiJhf58XcOjfm13kxt86N+XVeBTW3xXav5ty5c3Xp0iWNHj3a3jZnzhxVr15dvr6+kqTQ0FB17dpVq1at0iuvvJLneycnp8piseZpLL9Exrtw4Vqh3Zv5NR7z67yYW+fG/Dov5ta5Mb/OK69z6+rqcs8Fs2IZEHft2qVly5Zp8uTJevjhh+3t9evXdxjn7u6u4OBgJSQkFHWJAAAAAOB0DHsP4t388ssvioqKUteuXdW7d297e0ZGhrZv355j/OXLl5WRkVGUJQIAAACAUypWATE1NVVDhw5V+fLlNWXKFIe+lJQURUZGKj4+3t526tQpfffdd2rcuHFRlwoAAAAATqdYbTGdPn26EhMTNWPGDB07dszeXrlyZVWrVk1hYWEaNWqUIiIilJ2drWXLlsnLy0sDBgwwsGoAAAAAcA7FKiCuX79eFovF/p7EW15++WWNHDlSb731lqZMmaJ33nlH7u7uatu2rV599VVVqVLFoIoBAAAAwHkYFhDDw8NzvA/xhx9+uOc1vr6+mj17dmGWBQAAAAClVr6eQTx79qzS09Nz7Zs5c6ZmzpxZIEUBAAAAAIpevgJix44dtXHjxlz7qlSpolWrVhVIUQAAAACAopevgGi13v3l8uXKlZPZbP7VBQEAAAAAjHHfZxDPnj2rM2fO2H8+evSo9u3b5zAmLS1N//jHP9SsWbOCrxAAAAAAUCTuGxBXr16tmJgY+8+LFy/W4sWLc4xr0qSJ3nzzzYKtDgAAAABQZO4bEAcNGqSePXvKarWqU6dOmjhxojp27Ogwpnz58vL29i60IgEAAAAAhe++AdHHx0c+Pj72nytUqKAaNWoUalEAAAAAgKKXr0NqXn75ZQUGBhZWLQAAAAAAA913BfF2L7/8cmHVAQAAAAAwWL4C4qVLlzRv3jz98MMPun79eq5jNm/eXCCFAQAAAACKVr4C4oQJE7R9+3Z16tRJAQEBcnXN1w5VAAAAAEAxlq+AuHfvXr3wwgsaO3ZsYdUDAAAAADBIvpYAK1WqJD8/v0IqBQAAAABgpHyfYvr+++8rISGhsOoBAAAAABgkX1tMMzMz1bhxY/Xq1UudO3dWUFCQypcv7zDm6aefLtACAQAAAABFI18B8bXXXrP/c2xsrGJjYx36XVxcCIgAAAAAUELlKyCytRQAAAAAnBfvqQAAAAAASMrnCmJYWJhcXFzuOWbz5s2/qiAAAAAAgDHyFRB79uzpEBDT0tKUlJSk7du3KyAgQF26dCnwAgEAAAAARSNfAXHkyJG5ticlJWnUqFF6+OGHC6QoAAAAAEDRK5BnEAMCAjRjxgwtXry4IG4HAAAAADBAgR1S4+HhofPnzxfU7QAAAAAARSxfW0zXrFmTa/uFCxe0cuVKNWzYsCBqAgAAAAAYIF8BccKECbm2ly1bVk2bNtUbb7xREDUBAAAAAAyQr4CYkJBQYB+8Y8cO/eUvf9GmTZsc2tetW6f58+crJSVFXbt21WuvvSZPT0+HGt58800lJCSoSZMmmjFjhqpVq1ZgdQEAAABAafXAzyCePHlScXFxOnnyZL6vTUpKUlRUlCwWi0P7zp07FRUVpccee0zvvPOOjhw5ounTp9v7k5OTFRERIVdXV82fP181atTQ0KFDlZWV9aBfAwAAAABgk68VREn6+uuvNX36dJ0+fdre9vDDD2vSpEnq0KHDfa/fv3+/hgwZopo1ayolJcWh791331XLli01efJkSVLt2rXVtWtXjRw5Ug899JCWLVsms9msJUuWyMfHR48//rg6d+6sTZs2qVu3bvn9KgAAAACA2+RrBfHbb7/VSy+9JG9vb02cOFHz5s1TdHS0ypUrp5deeknbtm277z327dun8ePHq3///g7taWlp+vHHH/Xkk0/a22rWrKmAgADt3r1b0s0Vxnbt2snHx+dm8a6u6tChg3bt2pWfrwEAAAAAyEW+VhDnzZun5s2ba9myZXJ1/f9s+dxzz2nAgAF655131LZt23ve4/nnn5erq6tWr17t0H7x4kVZLBY9+uijDu3Vq1fX8ePHJUnnz59X586dc/R/8803+fkaAAAAAIBc5CsgJiUlacKECQ7hULq5kvfEE09o5syZ973Hndfekp6eLkn21cFbPD09denSJUlSRkbGPfvzyt/fO1/jYaxKlXzuPwglFvPrvJhb58b8Oi/m1rkxv86roOY2XwGxRo0a2rVrV47todLN7Z81atR44EI8PDwkSW5ubg7tJpPJHh5NJlOOfg8PD3t/XiUnp8piseZpLL9Exrtw4Vqh3Zv5NR7z67yYW+fG/Dov5ta5Mb/OK69z6+rqcs8Fs3w9gxgZGamNGzdq1KhR+uabb3To0CF98803GjlypLZs2aKhQ4fm53YO/P39Jd3cRnq7K1euqGzZsvYxd/ZfvnzZ3g8AAAAAeHD5WkHs1auXUlNTNX/+fG3cuFGSZLVa5eXlpejoaPXs2fOBC/H19VWdOnUUFxenVq1a2e8dHx+vli1bSpKaNGmiuLg4h+sOHTqkypUrP/DnAgAAAABuyvdrLgYNGqSnn35acXFxunTpkipUqKBmzZrJy8vrVxfTpUsXrVy5Uv369ZOvr6/Wr1+vixcvqk2bNpKkrl27KjIyUgcOHFBwcLDOnj2rLVu2aPTo0b/6swEAAACgtHug9yB+9tlnWrhwoSQpOjpaM2fO1LBhwxxeUfEgBg4cqM8//1xPP/20mjZtqtjYWIWFhSkoKEiSFBoaqtatW2vw4MEKCwvTzp07VbFiRfXp0+dXfS4AAAAAIJ/PIMbGxmrEiBFKSEiwt7Vs2VJlypTRK6+8oi1btvyqYvz9/fX555+radOmSkxM1KBBgzR37lx7v4uLi5YsWaL+/fsrMTFRrVq10j/+8Q95e3MqKQAAAAD8WvlaQVyyZIk6deqkBQsW2NvCw8MVHh6ukSNHauHChQoLC8vTvW5dd6cqVapo1qxZd73Ow8NDY8aM0ZgxY/JTOgAAAADgPvK1gnjixAm1a9dOLi4uOfp++9vf6ujRowVWGAAAAACgaOUrIFapUkU7d+7MtW/37t2cJgoAAAAAJVi+AuLgwYO1fv16jR07Vt9++63i4+P17bffKioqSl999ZUGDx5cWHUCAAAAAApZvp5B7Nu3r65evapFixYpNjZW0s13FZYpU0ZjxoxR3759C6VIAAAAAEDhy/drLoYMGaJnnnlG//3vf3X58mWVL19eISEh8vHxKYz6AAAAAABFJN8BUZJ8fHzUtm3bgq4FAAAAAGCgfD2DCAAAAABwXgREAAAAAIAkAiIAAAAAwIaACAAAAACQREAEAAAAANgQEAEAAAAAkgiIAAAAAAAbAiIAAAAAQBIBEQAAAABgQ0AEAAAAAEgiIAIAAAAAbAiIAAAAAABJBEQAAAAAgA0BEQAAAAAgiYAIAAAAALAhIAIAAAAAJBEQAQAAAAA2BEQAAAAAgCQCIgAAAADAxt3oAm63Z88eDRw4MNe+nj17auzYsWrbtm2Ovg0bNqhWrVqFXR4AAAAAOLViFRAbNWqkzz77zKHNYrFo2LBhatCggeLj41WxYkUtXbrUYUy1atWKskwAAAAAcErFKiB6e3srODjYoW3NmjUymUx65pln9MEHHygwMDDHGAAAAADAr1esn0E0m81avHixhg0bJk9PT8XHxysoKMjosgAAAADAKRXrgPj1118rJSVFvXr1kiTFx8dr69atat26tUJCQjR8+HAdP37c2CIBAAAAwEkUqy2md1qxYoV69uypsmXLKiUlRWfOnFFYWJiioqJ048YNLViwQEOGDNH69evl5uaW5/v6+3sXYtUoaJUq+RhdAgoR8+u8mFvnxvw6L+bWuTG/zqug5rbYBsSTJ09q586dmjRpkqSbzyeuWrVKQUFBcnW9ufDZsGFDde7cWdu3b1f79u3zfO/k5FRZLNY8jeWXyHgXLlwrtHszv8Zjfp0Xc+vcmF/nxdw6N+bXeeV1bl1dXe65YFZst5iuXbtW9erVU0BAgCTJw8NDjRs3todDSXrkkUfk7++vhIQEo8oEAAAAAKdRbANibGysOnfubP85OTlZcXFxDmOysrKUmpqqjIyMoi4PAAAAAJxOsQyIp06dUmJiojp06GBv2759u0aOHKn09HR72/r165Wenq4mTZoYUCUAAAAAOJdi+Qzi7t27VbZsWTVo0MDe1rFjR82dO1dDhw7VU089pbNnz+qDDz5Q06ZN1bZtWwOrBQAAAADnUCxXEPfs2aPg4GC5u/9/fvX29tZ7770nq9WqKVOmaPXq1YqIiNCyZcscnksEAAAAADyYYrmC+Pbbb+faXq9ePS1fvryIqwEAAACA0oGlNwAAAACAJAIiAAAAAMCGgAgAAAAAkERABAAAAADYEBABAAAAAJIIiAAAAAAAGwIiAAAAAEASAREAAAAAYENABAAAAABIIiACAAAAAGwIiAAAAAAASQREAAAAAIANAREAAAAAIImACAAAAACwISACAAAAACQREAEAAAAANgREAAAAAIAkAiIAAAAAwIaACAAAAACQREAEAAAAANgQEAEAAAAAkgiIAAAAAAAbAiIAAAAAQBIBEQAAAABgQ0AEAAAAAEgqhgHxyy+/VGBgoMOfdu3a2fvXrVunLl26qGXLlvrzn/+sjIwMA6sFAAAAAOfhbnQBd4qPj1fbtm31pz/9yd5mMpkkSTt37lRUVJR69+6t3//+91q4cKGmT5+uN99806hyAQAAAMBpFLuAePjwYbVq1UrBwcE5+t599121bNlSkydPliTVrl1bXbt21ciRI/XQQw8VdakAAAAA4FSK3RbT+Ph4BQUF5WhPS0vTjz/+qCeffNLeVrNmTQUEBGj37t1FWSIAAAAAOKViFRBPnz6tK1euKCYmRiEhIQoNDdXkyZOVmpqqixcvymKx6NFHH3W4pnr16jp+/LgxBQMAAACAEylWW0wPHDggFxcXtWjRQqNHj9bx48c1Z84c3bhxQxEREZIkHx8fh2s8PT116dKlfH2Ov793QZWMIlCpks/9B6HEYn6dF3Pr3Jhf58XcOjfm13kV1NwWq4AYGhqqL774wr5K2KZNG3l4eOjNN99Uv379JElubm4O15hMJqWnp+frc5KTU2WxWPM0ll8i4124cK3Q7s38Go/5dV7MrXNjfp0Xc+vcmF/nlde5dXV1ueeCWbHaYurn55djC2lISIgyMzNlNpslSefPn3fov3LlisqWLVtkNQIAAACAsypWATEpKUlHjx51aLt8+bIkKSMjQ3Xq1FFcXJy9z2q1Kj4+XpUrVy7KMgEAAADAKRWrgPjhhx9q9uzZDm1r1qyRyWRSw4YN1aVLF61cuVJXr16VJK1fv14XL15UmzZtjCgXAAAAAJxKsXoGsW/fvurfv79ef/11NWnSRHv37tWaNWsUGRkpX19fDRw4UJ9//rmefvppNW3aVLGxsQoLC8v1tRgAAAAAgPwpViuIjRs31rx587Rv3z795S9/0cGDBzVjxgy98sorkiR/f399/vnnatq0qRITEzVo0CDNnTvX4KoBAAAAwDkUqxVESerUqZM6dep01/4qVapo1qxZRVgRAAAAAJQOxWoFEQAAAABgHAIiAAAAAEASAREAAAAAYENABAAAAABIIiACAAAAAGwIiAAAAAAASQREAAAAAIANAREAAAAAIImACAAAAACwISACAAAAACQREAEAAAAANgREAAAAAIAkAiIAAAAAwIaACAAAAACQREAEAAAAANgQEAEAAAAAkgiIAAAAAAAbAiIAAAAAQBIBEQAAAABgQ0AEAAAAAEgiIAIAAAAAbAiIAAAAAABJBEQAAAAAgA0BEQAAAAAgSXI3uoA7JScn680339S2bdtkNpvVpk0bTZs2Tf7+/jp//rzatm2b45oNGzaoVq1aBlQLAAAAAM6j2AXE0aNH6/Tp0xozZoysVqsWLFig6OhoLV26VPHx8apYsaKWLl3qcE21atUMqhYAAAAAnEexCoi7du3SwYMHFRsbq6pVq0qSrFarZs6cqbS0NMXHxyswMFDBwcEGVwoAAAAAzqdYPYMYFBSkVatW2cOhJPn5+clischsNis+Pl5BQUEGVggAAAAAzqtYBUQfHx/VrVvXoW3btm2qW7eufH19FR8fr61bt6p169YKCQnR8OHDdfz4cWOKBQAAAAAnU6wC4p2OHDmi2NhYRUREKCUlRWfOnFGNGjU0c+ZMzZgxQ6dOndKQIUNkNpuNLhUAAAAASrxi9Qzi7cxmsyZOnKhHH31UPXv2lMVi0apVqxQUFCRX15u5tmHDhurcubO2b9+u9u3b5/ne/v7ehVU2CkGlSj5Gl4BCxPw6L+bWuTG/zou5dW7Mr/MqqLkttgFx0aJF+umnn7Rq1Sq5u98ss3Hjxg5jHnnkEfn7+yshISFfATE5OVUWizVPY/klMt6FC9cK7d7Mr/GYX+fF3Do35td5MbfOjfl1XnmdW1dXl3sumBXLLabbt2/XokWLFB0drcDAQEk3348YFxfnMC4rK0upqanKyMgwokwAAAAAcCrFLiAmJiZq9OjR6t69u/r162dv3759u0aOHKn09HR72/r165Wenq4mTZoYUSoAAAAAOJVitcU0KytLo0aNkoeHh/r27asDBw7Y+zp27Ki5c+dq6NCheuqpp3T27Fl98MEHatq0qdq2bWtg1QAAAADgHIpVQExMTNSxY8ckSc8995xD38cff6z33ntPkydP1pQpU1ShQgVFREToxRdftB9aAwAAAAB4cMUqIDZq1Eg//fTTPccsX768iKoBAAAAgNKFpTcAAAAAgCQCIgAAAADAhoAIAAAAAJBEQAQAAAAA2BAQAQAAAACSCIgAAAAAABsCIgAAAABAEgERAAAAAGBDQAQAAAAASCIgAgAAAABsCIgAAAAAAEkERAAAAACADQERAAAAACCJgAgAAAAAsCEgAgAAAAAkERABAAAAADYERAAAAACAJAIiAAAAAMCGgAgAAAAAkERABAAAAADYEBABAAAAAJIIiAAAAAAAGwIiAAAAAEASAREAAAAAYENABAAAAABIKoEB0WKxaN68efrtb3+rtm3b6uOPPza6JAAAAABwCu5GF5BfCxYs0Pvvv69x48apRo0aev311/XQQw/piSeeMLo0AAAAACjRSlRATE1N1QcffKDhw4crIiLC3rZw4UICIgAAAAD8SiVqi+n333+vjIwMPfnkk/a2sLAw/fzzz/rll18MrAwAAAAASr4SFRDPnz8vT09P1apVy95Wvnx5eXl56eTJkwZWBgAAAAAlX4naYpqeni4fHx+5uLg4tJcpU0YpKSl5vo+rq8v9B93moQrl8jUeBSu/85VfzK+xmF/nxdw6N+bXeTG3zo35dV55ndv7jXOxWq3WgiioKKxcuVIxMTHatm2bQ3u7du0UFRWlHj16GFQZAAAAAJR8JWqLqb+/v1JSUmQ2mx3ar169Ki8vL4OqAgAAAADnUKICYnBwsMxms/bv329vS0pK0o0bN1S5cmUDKwMAAACAkq9EBcQqVaooJCRE7733nr3t73//u8qXL69GjRoZWBkAAAAAlHwl6hlESdq9e7deeOEFNWvWTGXLltW3336r6Oho+3sRAQAAAAAPpsQFREmKi4tTTEyMrl69qj59+qh3795GlwQAAAAAJV6JDIgAAAAAgIJXop5BBAAAAAAUHgIiAAAAAEASAREAAAAAYENAdHI7duxQp06djC4DBSg5OVmjRo1SSEiIGjdurOHDhys5OdnoslCAdu3apYkTJ2rs2LH65z//KYvFYnRJKGBms1nh4eFasGCB0aWggHz55ZcKDAx0+NOuXTujy0IBs1gsCg8P17hx44wuBQVgz549OX5vb/2ZMGGC0eUZxt3oAlB4kpKSFBUVJS8vL6NLQQEaPXq0Tp8+rTFjxshqtWrBggWKjo7W0qVLjS4NBeA///mPJk6cqB49eqhixYqaNm2aEhIS9PrrrxtdGgrQ+++/r0OHDul3v/ud0aWggMTHx6tt27b605/+ZG8zmUwGVoTCsGLFCh07dkyLFy82uhQUgEaNGumzzz5zaLNYLBo2bJgaNGhgUFXGIyA6qf3792vIkCGqWbOmUlJSjC4HBWTXrl06ePCgYmNjVbVqVUmS1WrVzJkzlZaWpnLlyhlcIX6NzMxMTZs2Ta+//rp69eolSapTp45mzpyp6Oho/semk0hKSlJMTAy/r07m8OHDatWqlYKDg40uBYXkwoULmjdvnoYNG6YqVaoYXQ4KgLe3d47f2TVr1shkMumZZ54xqCrjscXUSe3bt0/jx49X//79jS4FBSgoKEirVq2yh0NJ8vPzk8VikdlsNrAyFIT09HSNGjVKf/zjH+1tlStXVmZmpnFFoUBZLBZNnDhRXbt2VaNGjYwuBwUoPj5eQUFBRpeBQjRjxgyVL19egwcPNroUFBKz2azFixdr2LBh8vT0NLocwxAQndTzzz+v8PBwo8tAAfPx8VHdunUd2rZt26a6devK19fXoKpQUHx9ffXMM8/Izc1N0s3AuGLFCnXo0IHVQyfx0Ucf6cyZM3rttdeMLgUF6PTp07py5YpiYmIUEhKi0NBQTZ48WampqUaXhgKye/du/ec//1G1atUUHR2tuXPnskPLCX399ddKSUmx7+IprQiITsrVlaktDY4cOaLY2FhFREQYXQoKWExMjDp37qyLFy9q+vTpRpeDAnDixAm9++67mjp1qsqXL290OShABw4ckIuLi1q0aKGFCxdq5MiR+ve//61p06YZXRoKyOzZsyXdPCguOTlZH374of74xz/q/PnzBleGgrRixQr17NlTZcuWNboUQ5EigBLKbDZr4sSJevTRR9WzZ0+jy0EBCw4OVsuWLXXs2DFt2LDB6HLwK1mtVk2aNEndunVThw4djC4HBSw0NFRffPGFXn31VbVp00b9+/dXdHS01q5dq6tXrxpdHn6lgwcP6uDBg+revbtiY2P10Ucfac2aNbp27RoHxDmRkydPaufOnerbt6/RpRiOQ2qAEmrRokX66aeftGrVKrm786vsbNq3b6/27dsrKChIU6dOVbdu3Vh1KsE++eQTnTp1ipMPnZSfn5/8/Pwc2kJCQpSZmamkpCSFhIQYUxgKxPHjxyXdfHznloCAADVv3lzx8fEGVYWCtnbtWtWrV08BAQFGl2I4VhCBEmj79u1atGiRoqOjFRgYaHQ5KCDZ2dk6ffq0Q1uHDh2UlZWlU6dOGVQVCsJXX32lc+fOqUWLFvZ3bO3du1cxMTH8DjuBpKQkHT161KHt8uXLkqSMjAwDKkJBurXd8OGHH3Zo9/T0lIeHhxEloRDExsaqc+fORpdRLLDsAJQwiYmJGj16tLp3765+/foZXQ4K0P79+zVw4ECtX7/e/j9Ejh8/LldXV1WrVs3g6vBrTJ06VdevX3domzRpkoKCgvg9dgIffvihUlJSHFaIbx2V37BhQwMrQ0EICgqSi4uLEhISFBoaKulm8N+/fz8HAjqJU6dOKTExkWf+bQiIQAmSlZWlUaNGycPDQ3379tWBAwfsfXXq1JG3t7eB1eHXCgkJUcOGDTV06FCNHTtWkjR9+nR1795d/v7+BleHX6NWrVo52sqVK6dKlSqV6pcxO4u+ffuqf//+ev3119WkSRPt3btXa9asUWRkJCdMO4EqVaroD3/4g1577TVFRUXJ19dXy5Yt0/Xr10v1u/Kcye7du1W2bFn+PrYhIAIlSGJioo4dOyZJeu655xz6Pv74Yz322GNGlIUC4uLiokWLFmnGjBmaOHGiPDw81KNHD40aNcro0gDcQ+PGjTVv3jzNmTNHq1evVq1atTRjxgxWl5zI1KlTtXDhQr399tu6ePGi6tatq6VLl7K7w0ns2bNHwcHBnOlg42K1Wq1GFwEAAAAAMB6H1AAAAAAAJBEQAQAAAAA2BEQAAAAAgCQCIgAAAADAhoAIAEAhOH78uFavXq3MzEx729q1axUXF2dgVQAA3BtnuQIAcIejR4+qW7du+bomNjZWv/nNb+w///e//1V0dLS6du0qDw8PSdJbb72lzp07KyQkRElJSZo/f36O+zRo0EAjRozI0f7zzz+re/fud/38Tz/9VCEhIfmqGQCAOxEQAQC4Q9myZSVJr776qrp06XLPsV999ZVmzZplD4GSlJKSouzsbElSVlaW0tLSJElubm5ydXXV5cuXlZqaqq+++kpDhgxRrVq1JEnvv/++/bo7mUwmSdKzzz6r5s2b29vj4uK0fPlyXsgOACgQBEQAAO7g6pr/JzDc3Nzs/xwZGalDhw5Jklq1auUwbvny5frhhx80depUSVKHDh3UokULSdIXX3xhD4J3q6lp06Y5VhKXL1/uEFABAHhQBEQAAO7g4uIiSZo1a5ZmzZqVr2sk6eOPP9bWrVs1ZswYHTp0SO7uN/9zGxYWpq5du+rFF1/U6dOnc73P/cLp7Z+Tl3YAAPKDgAgAwB0sFoskacaMGQoPD7/n2NWrVys6Olpms9ne5u3tbQ96165ds68uWiwWubq6ytvb2x7orl+/rqtXr0rSXbeX3u5BVjcBAMgrAiIAAHdIT0+XJEVHRys6OjpP19x+WuntWrdunWv7rYA4ZMgQh/Ynnngi1/G3AigBEQBQmAiIAADcoUaNGtq8eXO+rqlSpUqu7bdvMX3yySft7VarVZL0ySef2J9BHDBgwF3vfyu0lilTJl91AQCQHwREAADuYDKZtH//fiUlJenZZ59VxYoVJUlTp05VfHy8VqxYked7NWrUyOHnDh06SJLDltS8uHLliqS7B1EAAAoCAREAgDukpaXp7bffliT99re/1aVLlyRJp0+f1unTp5WUlCTp5iqg1WqV2WyWl5eXHnnkkRz32r17t/0ZxGeeecbefmtL6vr163Xw4EFJ0rlz5/TQQw/lWtOZM2ckSVWrVi2IrwgAQK4IiAAA3GHmzJk6d+6cvLy8NHToUHt7enq6zGaz+vTpI+n/D7PJzs5WWFiY5s+fr+vXr+v06dNKTk6WdPOQmlvPDZrNZl29elXHjh2zvxtxz549+uGHHyRJFy9evGtN3333nfz8/OyrmQAAFAYCIgAAt/nnP/+plStXavr06erVq5dD34QJE/T9999r48aNd73+yJEjeu6551SmTBn5+fmpd+/e9j6r1ap///vf+v7779W/f3+5uLho5cqV8vLyknT3ZxDT09O1ceNGhYaGFsA3BADg7giIAADYnDt3TlOnTtWTTz6pLl266NChQzKZTPYtoteuXVN2drbDFtPs7GxlZmaqfv368vDwUJMmTXTgwAFduXJF69evV+/eveXq6qpTp07p2LFjateunSRp9OjReuSRR+zh8F6WL1+uq1ev6g9/+EOevsff/vY3SVKfPn1Urly5B/lXAQAopQiIAADYVK1aVX/729/UoEEDHTx4UIMHD5bJZLJvEc3IyMixxdRqtSorK0ubN292eD7wrbfe0rfffqsWLVooICBAGzdu1Ny5c7V06VIFBgbq66+/Vv/+/e9bU1JSkpYsWaL69esrLCzsruNunYoqSfv371dsbKx69OhBQAQA5AsBEQCA2zRv3lyS1KpVK/vhMbfkZYupJH366af68ssv9dFHHykgIECSNHjwYP3www+aOnWqGjdurKysLIftp5JjyJOkEydOaMSIEcrMzNT06dNzfQfirQD4zjvvqFmzZrp+/bq++eYbtWzZkucVAQD5RkAEAOA2e/bskaenp7y8vOxbS2+5c4vp7TIzM3XlyhX95je/0fz58zVp0iT7+w1vmTJlij799FPNmzdPffv21W9+8xtJ0vXr1zVp0iTt37/fvjp5+vRp9erVS9evX9fcuXNzvC7jltatWys0NFQbN27UunXr5ObmpurVq2vkyJEF8a8DAFDKuFjv/L8rAQAoxR577DFdu3Yt14B4L9nZ2UpPT9ehQ4eUmpoqb2/vXMcdOXJEixcv1rRp01S2bFl7+7hx4+Tm5qZXX31V/v7+kqQNGzbo2rVrOQ7LAQCgsBAQAQC4TXp6usqUKWN0GQAAGIKACAAAAACQJOV82h0AAAAAUCoREAEAAAAAkgiIAAAAAAAbAiIAAAAAQBIBEQAAAABg83/exRYtzwNDqgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_weekday_cnt = data.groupby('星期几', as_index=False)['开始时间'].count()\n",
    "data_weekday_cnt.columns = ['星期几','count']\n",
    "#data_weekday_cnt = data_weekday_cnt.sort_values('count', ascending=False)\n",
    "#data_weekday_cnt.reset_index(inplace=True)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(15,6))\n",
    "\n",
    "ax = sns.barplot(x=\"星期几\", y=\"count\", data=data_weekday_cnt, color='b')\n",
    "\n",
    "\n",
    "ax.set_title('一周出行分布')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "出行高峰出现在周五"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 一天出行分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>开始时间</th>\n",
       "      <th>结束时间</th>\n",
       "      <th>类别</th>\n",
       "      <th>起始位置</th>\n",
       "      <th>结束位置</th>\n",
       "      <th>里程</th>\n",
       "      <th>目的</th>\n",
       "      <th>月</th>\n",
       "      <th>月份</th>\n",
       "      <th>星期几</th>\n",
       "      <th>小时</th>\n",
       "      <th>里程打标</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01 21:11:00</td>\n",
       "      <td>2016-01-01 21:17:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>5.1</td>\n",
       "      <td>Meal/Entertain</td>\n",
       "      <td>1</td>\n",
       "      <td>一月</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>0-10 miles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-02 01:25:00</td>\n",
       "      <td>2016-01-02 01:37:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>一月</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0-10 miles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-02 20:25:00</td>\n",
       "      <td>2016-01-02 20:38:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Errand/Supplies</td>\n",
       "      <td>1</td>\n",
       "      <td>一月</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>0-10 miles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-05 17:31:00</td>\n",
       "      <td>2016-01-05 17:45:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Meeting</td>\n",
       "      <td>1</td>\n",
       "      <td>一月</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>0-10 miles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-06 14:42:00</td>\n",
       "      <td>2016-01-06 15:49:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>63.7</td>\n",
       "      <td>Customer Visit</td>\n",
       "      <td>1</td>\n",
       "      <td>一月</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>above 50 miles</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 开始时间                结束时间        类别         起始位置  \\\n",
       "0 2016-01-01 21:11:00 2016-01-01 21:17:00  Business  Fort Pierce   \n",
       "1 2016-01-02 01:25:00 2016-01-02 01:37:00  Business  Fort Pierce   \n",
       "2 2016-01-02 20:25:00 2016-01-02 20:38:00  Business  Fort Pierce   \n",
       "3 2016-01-05 17:31:00 2016-01-05 17:45:00  Business  Fort Pierce   \n",
       "4 2016-01-06 14:42:00 2016-01-06 15:49:00  Business  Fort Pierce   \n",
       "\n",
       "              结束位置    里程               目的  月  月份  星期几  小时            里程打标  \n",
       "0      Fort Pierce   5.1   Meal/Entertain  1  一月    5  21      0-10 miles  \n",
       "1      Fort Pierce   5.0              NaN  1  一月    6   1      0-10 miles  \n",
       "2      Fort Pierce   4.8  Errand/Supplies  1  一月    6  20      0-10 miles  \n",
       "3      Fort Pierce   4.7          Meeting  1  一月    2  17      0-10 miles  \n",
       "4  West Palm Beach  63.7   Customer Visit  1  一月    3  14  above 50 miles  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取hour\n",
    "data['小时'] = data['开始时间'].dt.hour\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_hour_cnt = data.groupby('小时', as_index=False)['开始时间'].count()\n",
    "data_hour_cnt.columns = ['小时','count']\n",
    "\n",
    "\n",
    "\n",
    "plt.figure(figsize=(15,6))\n",
    "\n",
    "ax = sns.barplot(x=\"小时\", y=\"count\", data=data_hour_cnt, color='b')\n",
    "\n",
    "\n",
    "ax.set_title('一天出行分布')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>起始位置</th>\n",
       "      <th>开始时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Cary</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Unknown Location</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Islamabad</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Morrisville</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Whitebridge</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Durham</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                起始位置  开始时间\n",
       "8               Cary    64\n",
       "83  Unknown Location    47\n",
       "32         Islamabad    24\n",
       "48       Morrisville    24\n",
       "91       Whitebridge    23\n",
       "14            Durham    14"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['小时'].isin(['13','15','17','18'])].groupby('起始位置', as_index=False)['开始时间'].count().sort_values('开始时间', ascending=False).head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一天中乘客的出行高峰出现在下午3点，针对该时间高峰需提前调度车辆至Cary、Islamabad、Morrisville、Whitebridge和Durham等地方，以满足高峰期出行订单需求"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 出行里程数分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of                     开始时间                结束时间        类别              起始位置  \\\n",
       "0    2016-01-01 21:11:00 2016-01-01 21:17:00  Business       Fort Pierce   \n",
       "1    2016-01-02 01:25:00 2016-01-02 01:37:00  Business       Fort Pierce   \n",
       "2    2016-01-02 20:25:00 2016-01-02 20:38:00  Business       Fort Pierce   \n",
       "3    2016-01-05 17:31:00 2016-01-05 17:45:00  Business       Fort Pierce   \n",
       "4    2016-01-06 14:42:00 2016-01-06 15:49:00  Business       Fort Pierce   \n",
       "...                  ...                 ...       ...               ...   \n",
       "1150 2016-12-31 01:07:00 2016-12-31 01:14:00  Business           Kar?chi   \n",
       "1151 2016-12-31 13:24:00 2016-12-31 13:42:00  Business           Kar?chi   \n",
       "1152 2016-12-31 15:03:00 2016-12-31 15:38:00  Business  Unknown Location   \n",
       "1153 2016-12-31 21:32:00 2016-12-31 21:50:00  Business        Katunayake   \n",
       "1154 2016-12-31 22:08:00 2016-12-31 23:51:00  Business           Gampaha   \n",
       "\n",
       "                  结束位置    里程               目的   月   月份  星期几  小时  \\\n",
       "0          Fort Pierce   5.1   Meal/Entertain   1   一月    5  21   \n",
       "1          Fort Pierce   5.0              NaN   1   一月    6   1   \n",
       "2          Fort Pierce   4.8  Errand/Supplies   1   一月    6  20   \n",
       "3          Fort Pierce   4.7          Meeting   1   一月    2  17   \n",
       "4      West Palm Beach  63.7   Customer Visit   1   一月    3  14   \n",
       "...                ...   ...              ...  ..  ...  ...  ..   \n",
       "1150           Kar?chi   0.7          Meeting  12  十二月    6   1   \n",
       "1151  Unknown Location   3.9   Temporary Site  12  十二月    6  13   \n",
       "1152  Unknown Location  16.2          Meeting  12  十二月    6  15   \n",
       "1153           Gampaha   6.4   Temporary Site  12  十二月    6  21   \n",
       "1154         Ilukwatta  48.2   Temporary Site  12  十二月    6  22   \n",
       "\n",
       "                里程打标  \n",
       "0         0-10 miles  \n",
       "1         0-10 miles  \n",
       "2         0-10 miles  \n",
       "3         0-10 miles  \n",
       "4     above 50 miles  \n",
       "...              ...  \n",
       "1150      0-10 miles  \n",
       "1151      0-10 miles  \n",
       "1152     10-20 miles  \n",
       "1153      0-10 miles  \n",
       "1154     40-50 miles  \n",
       "\n",
       "[1150 rows x 12 columns]>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['里程打标'] = pd.cut(x = data['里程'], bins = [0, 10, 20, 30, 40, 50, 100000], right = False, labels = ['0-10 miles', '10-20 miles', '20-30 miles ', '30-40 miles', '40-50 miles', 'above 50 miles'])\n",
    "data.head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_miles_cnt = data.groupby('里程打标', as_index=False)['开始时间'].count()\n",
    "data_miles_cnt.columns = ['里程打标','count']\n",
    "data_miles_cnt = data_miles_cnt.sort_values('count', ascending=False)\n",
    "data_miles_cnt.reset_index(inplace=True)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(15,6))\n",
    "\n",
    "ax = sns.barplot(x=\"里程打标\", y=\"count\", data=data_miles_cnt, palette='rocket')\n",
    "\n",
    "\n",
    "ax.set_title('出行里程数分布')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由上图可知，大部分订单主要是以短途出行为主"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## 总结\n",
    "- ### 运营情况：\n",
    "2016年，Uber业务呈季节性波动，对于淡旺季、节假日进行精细化运营，优惠券、动态价格政策等运营方式，针对问题实施合理对策解决，提高业务量\n",
    "- ### 用户出行特征：\n",
    "合理利用用户行为数据，大多数用户因商务短途出行为主，多数会跨城，出行时间主要集中在下午，结合季节性出行特征，可以构建模型并进行预测，指导网约车调度及业务策略"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
